SCGAN

Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

Prepare

Put the G.pth and VGG weights in "./checkpoints" and "./" respectively.

Environments:python=3.8, pytorch=1.6.0, Ubuntu=20.04.1 LTS

Put the train-list of makeup images in "./MT-Dataset/makeup.txt" and the train-list of non-makeup images in "./MT-Dataset/non-makeup.txt"

Use the "./scripts/handle_parsing.py" to convert the origin MT-Dataset's seg labels

Use python sc.py --phase train to train

python sc.py --phase test

python sc.py --phase test --partial

python sc.py --phase test --interpolation

python sc.py --phase test --partial --interpolation

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.idea		.idea
MT-Dataset		MT-Dataset
SCDataset		SCDataset
models		models
options		options
results		results
scripts		scripts
LICENSE		LICENSE
README.md		README.md
global_interpolation_transferred.jpg		global_interpolation_transferred.jpg
global_transferred.jpg		global_transferred.jpg
partial_interpolation_transferred.jpg		partial_interpolation_transferred.jpg
partial_transferred.jpg		partial_transferred.jpg
sc.py		sc.py
test.txt		test.txt